Goal/Purpose of operations:
The DepMap/PRISM’s primary pooled drug screens were used to help evaluate if a candidate could be a suitable candidate (if that drug was tested). The primary screen calculated the median of log fold change median fluorescence intensity between replicates of a cell line treated with a drug. The PRISM study considered a cell line as sensitive to a treatment if the median-collapsed fold-change is less than 0.3.
Finished psedocode on:
220503
System which operations were done on:
my laptop
GitHub Repo:
Transfer_Learning_R03
Docker:
rstudio_cancer_dr
Directory of operations:
/home - docker
Scripts being edited for operations:
NA
Data being used:
PRISM/DEPMAP data downloaded from depmap.org data explore tool. 220503- download data
/output/TF_L_GBM/220503_PRISM_DEPMAP_candidate_data/
Papers and tools:
NA
primary_gbm_res <- read.csv(file= "~/output/220808_prism_gbm_candidates.csv" )
library(readr)
cell_gbm_info<- read_csv("~/data/DEPMAP_PRISM_220228/cell-line-selector_gbm_top_ten.csv")
## Rows: 61 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): lineage3, depmapId, displayName, Tumor Type, Gender, Microsatellit...
## dbl (23): temozolomide Drug sensitivity (PRISM Repurposing Primary Screen) 1...
## lgl (1): LongTable-checkbox
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
remove the odd names
cell_gbm_info<- cell_gbm_info[, -c(1:3, 5)]
library(tidyr)
cell_gbm_info_longer <- pivot_longer(cell_gbm_info, c(2:12), names_to = "drug", values_to = "log2change")
removing the extra info in the drug stuff
cell_gbm_info_longer$drug <- sub(" .*", "", cell_gbm_info_longer$drug)
remove NAs from the logfold change data
cell_gbm_info_longer_v2 <- cell_gbm_info_longer[ ! is.na(cell_gbm_info_longer$log2change),]
library(ggplot2)
library(cowplot)
library(viridis)
## Loading required package: viridisLite
library(circlize)
## ========================================
## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
library(ComplexHeatmap)
## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.10.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= drug, color= drug))+ geom_point(stat = "identity") +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= drug)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1, p2, ncol = 1, align = "v")
cell_gbm_info_longer_v2$tmz_cell<- ifelse(cell_gbm_info_longer_v2$displayName %in% c("A172", "GB1","KNS81", "SF295", "YH13", "YKG1"), "TMZ_RES", "TMZ_Sen")
gbm_candidate_prism_plot_tmz <- function(cell_line_info, drug){
cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug %in% c(drug, "temozolomide"),]
#cell_gbm_info_longer_v2$shape <- ifelse(cell_gbm_info_longer_v2$tmz_cell== "TMZ_RES", 19, 17)
cell_gbm_info_longer_v2$tmz_cell<- factor(cell_gbm_info_longer_v2$tmz_cell, levels= c("TMZ_Sen", "TMZ_RES" ))
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= drug, shape= tmz_cell))+ geom_point(stat = "identity", size = 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) #+ theme(axis.text.y = element_text( color = a))
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= drug)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1+ scale_fill_manual( values=c("#440154FF","#228C8DFF")), p2+ scale_color_manual( values= c("#440154FF","#228C8DFF") ), ncol = 1, align = "v")
}
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "simvastatin")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "pamidronate")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "floxuridine")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "nimodipine")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "vardenafil")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "moxifloxacin")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "diltiazem")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "diflunisal")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "saxagliptin")
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "icosapent")
want to determine if there is a difference between groups and drug repesonse
#mgmt methylation
cell_gbm_info_longer_v2$MGMT_METH<- ifelse(cell_gbm_info_longer_v2$`MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504` >0.5, "Hyper methylation (>0.5)", "Hypo methylation (=< 0.5)")
drugs<- unique(cell_gbm_info_longer_v2$drug)
mgmt<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$MGMT_METH),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$MGMT_METH == "Hyper methylation (>0.5)",1]
y<- test[!cell_line_info$MGMT_METH == "Hyper methylation (>0.5)",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
mgmt[i]<- res$p.value
}
}
#egfr COPY NUMBER
cell_gbm_info_longer_v2$EGFR_copy <- ifelse(cell_gbm_info_longer_v2$`EGFR (ERBB1, ERBB) Copy Number 22Q2 Public` >2, "Copy Number > 2", "Copy Number =< 2")
egfr_amp<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$EGFR_copy ),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$EGFR_copy == "Copy Number > 2",1]
y<- test[!cell_line_info$EGFR_copy == "Copy Number > 2",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
egfr_amp[i]<- res$p.value
}else{
print(i)
}
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
#note most of the gbm cell lines have no egfr amplication
#table(cell_gbm_info_longer_v2$Gender)
sex<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$Gender ),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$Gender == "Male",1]
y<- test[!cell_line_info$Gender == "Male",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
sex[i]<- res$p.value
}else{
print(i)
}
}
#tmz non-senssitive cell lines (0.3=> logfoldchange in PRISM)
#A172
#GB1
#KNS81
#SF295
#YH13
#YKG1
cell_gbm_info_longer_v2$tmz_cell<- ifelse(cell_gbm_info_longer_v2$displayName %in% c("A172", "GB1","KNS81", "SF295", "YH13", "YKG1"), "TMZ_RES", "TMZ_Sen")
tmz<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$tmz_cell ),]
#groups<-
test<- as.vector(as.data.frame(cell_line_info[,18]))
x<- test[cell_line_info$tmz_cell == "TMZ_RES",1]
y<- test[!cell_line_info$tmz_cell == "TMZ_RES",1]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
tmz[i]<- res$p.value
}else{
print(i)
}
}
drugs<- unique(cell_gbm_info_longer_v2$drug)
index<- c(3,4,10,11,12)
test_res<- matrix(nrow= 11, ncol= 5)
for( i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
for(j in 1:5){
test<- cell_line_info[,c(index[j], 18)]
groups<- test[,1]== 1
x<- test[groups,2]
y<- test[!groups,2]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided")
test_res[i,j]<- res$p.value
}else{
res<- NA
test_res[i,j]<-res
}
}
}
rownames(test_res)<- drugs
colnames(test_res)<- colnames(cell_gbm_info_longer_v2)[index]
test_res<- cbind(test_res, sex, tmz, mgmt)
test_res_filter_1<- test_res< 0.05
#p-value for the MGMT promotor
test_res_filter_2<- test_res< 0.16795666
gbm_candidate_prism_plot_two_groups <- function(cell_line_info, drug, info_index, divider= 0, group1="Normal", group2= "Mutation" ){
#index_col <- grepl(colnames(cell_line_info), info)
cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug == drug,]
a <- ifelse(cell_gbm_info_longer_v2[,info_index] ==divider , 19, 17)
cell_gbm_info_longer_v2$test <- ifelse(cell_gbm_info_longer_v2[,info_index] ==divider , group1, group2)
#print(a)
#nam <- colnames(cell_gbm_info_longer_v2)[info_index]
#cell_gbm_info_longer_v2[,info_index]<- as.factor(cell_gbm_info_longer_v2[,info_index])
#print(cell_gbm_info_longer_v2[,info_index])
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= test))+ geom_point(stat = "identity", shape = a, size = 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)
#+ theme(axis.text.y = element_text( color = a))
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= test)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1+ scale_fill_manual( values=c("#440154FF","#228C8DFF")), p2+ scale_color_manual( values= c("#440154FF","#228C8DFF") ), ncol = 1, align = "v")
}
#pten pamidronate
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 12 )
#Sex
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "vardenafil", 14, divider= "Male", group1="Male", group2= "Female")
cell_gbm_info_longer_v2<- cell_gbm_info_longer_v2[,c(1:18, 20,21, 19)]
different p-value threshold because of low poer. DO NOT TRUST THIS DATA
#"EGFR_copy"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "diflunisal", 21, divider= "TMZ_RES", group1="TMZ Resistant", group2= "TMZ Sensitive")
if they are reistant to tmz it looks reistant here.
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "icosapent", 21, divider= "TMZ_RES", group1="TMZ Resistant", group2= "TMZ Sensitive")
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "floxuridine", 19, divider= "Hyper methylation (>0.5)", group1="Hyper methylation (>0.5)", group2= "Hypo methylation (=< 0.5)")
## Warning: Removed 4 rows containing missing values (geom_point).
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 14, divider= "Male", group1="Male", group2= "Female")
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "icosapent", 14, divider= "Male", group1="Male", group2= "Female")
#pten
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 12 )
#"TP53 (LFS1, p53) Mutation 22Q2 Public
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 3 )
#"TP53 (LFS1, p53) Mutation 22Q2 Public
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "floxuridine", 3 )
#"RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 10 )
#"RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 10 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 11 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 11 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "temozolomide", 11 )
#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "simvastatin", 11 )
linear regression stuff with methylation of MGMT and efgr amplication
egfr_copy_p<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$`EGFR (ERBB1, ERBB) Copy Number 22Q2 Public` ),]
#groups<-
test<- as.data.frame(cell_line_info[,c(5, 18)])
res <- lm(log2change ~ `EGFR (ERBB1, ERBB) Copy Number 22Q2 Public`, test)
egfr_copy_p[i]<- summary(res)$coefficients[,4][2]
}
mgmt_methy_p<- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
#test<- cell_line_info[,c(index[j], 18)]
cell_line_info<- cell_line_info[!is.na(cell_line_info$`MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504` ),]
#groups<-
test<- as.data.frame(cell_line_info[,c(2, 18)])
res <- lm(log2change ~ `MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504`, test)
mgmt_methy_p[i]<- summary(res)$coefficients[,4][2]
}
test_res <- cbind(test_res, egfr_copy_p, mgmt_methy_p)
gbm_candidate_prism_plot_cont <- function(cell_line_info, drug, info_index, title ){
#index_col <- grepl(colnames(cell_line_info), info)
cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug == drug,]
nam <- colnames(cell_gbm_info_longer_v2)[info_index]
#cell_gbm_info_longer_v2[,info_index]<- as.factor(cell_gbm_info_longer_v2[,info_index])
#print(cell_gbm_info_longer_v2[,info_index])
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= get(nam)))+ geom_point(stat = "identity", size= 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)+ labs(color=title)
#+ theme(axis.text.y = element_text( color = a))
p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change)) + geom_density(alpha=.5) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
legend <- get_legend(p2)
p2<- p2 + theme(legend.position='none')
ggdraw(plot_grid(plot_grid(p1, p2, ncol=1, align='v'),
plot_grid(NULL, legend, ncol=1),
rel_widths=c(1, 0.2)))
}
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "moxifloxacin", 2, "MGMT Promoter Methylation(0-1)" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "floxuridine", 2, "MGMT Promoter Methylation(0-1)" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "floxuridine", 5, "EGFR Copy Number" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "moxifloxacin", 5, "EGFR Copy Number" )
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "nimodipine", 5, "EGFR Copy Number" )
change the tmz resistant test compare the log2fold changes of the resistnat cells lines between drugs not within a drug.
cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$tmz_cell =="TMZ_RES", ]
tmz_v2 <- c()
for (i in 1:length(drugs)){
cell_line_info<- cell_gbm_info_longer_v3[cell_gbm_info_longer_v3$drug %in% c(drugs[i], "temozolomide"),]
#simvastain does have the A172 cell line so
# only keep cell lines with duplicated
paired_cell_lines<- cell_line_info$displayName[duplicated(cell_line_info$displayName)]
cell_line_info<- cell_line_info[cell_line_info$displayName %in% paired_cell_lines,]
test<- as.vector(as.data.frame(cell_line_info[,17:18]))
x<- test[test$drug == drugs[i],2]
y<- test[!test$drug == drugs[i],2]
x<- as.numeric(unlist(x))
y<- as.numeric(unlist(y))
if(length(x) > 1 & length(y) > 1 ){
res <- wilcox.test(x,y,alternative = "two.sided", paired = TRUE)
tmz_v2[i]<- res$p.value
}else{
print(i)
}
}
## [1] 1
#replace the old tmz resistant p-values with the new p-values
colnames(test_res)
## [1] "TP53 (LFS1, p53) Mutation 22Q2 Public"
## [2] "EGFR (ERBB1, ERBB) Mutation 22Q2 Public"
## [3] "RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"
## [4] "MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
## [5] "PTEN (MHAM, PTEN1, MMAC1, BZS, TEP1) Mutation 22Q2 Public"
## [6] "sex"
## [7] "tmz"
## [8] "mgmt"
## [9] "egfr_copy_p"
## [10] "mgmt_methy_p"
tmz_v2[1]<- 1 # for temzolomide
test_res[,7]<- tmz_v2
#plot.data$stars <- cut(plot.data$p.value, breaks=c(-Inf, 0.001, 0.01, 0.05, Inf), label=c("***", "**", "*", ""))
test_res_t <- t(test_res)
rownames(test_res_t)<- c("TP53 Mutation - Wilcox", "EGFR Mutation - Wilcox" , "RB1 Mutation - Wilcox", "MTOR Mutation - Wilcox", "PTEN Mutation - Wilcox", "Sex - Wilcox", "TMZ-Resistant - Wilcox Paired", "MGMT Promoter Methylation (high vs low) - Wilcox", "EGFR Copy Number - linear regression", "MGMT Promoter Methylation- linear regression" )
col_fun = colorRamp2(c(0, 1), c("blue", "white"))
Heatmap(test_res_t, nam= "p-value of biomarker and PRISM Drug Response", col = col_fun, column_names_rot = 45,
clustering_distance_rows= "euclidean",
clustering_distance_columns= "euclidean",
clustering_method_rows = "ward.D2" ,
clustering_method_columns="ward.D2",
cell_fun = function(j, i, x, y, w, h, fill) {
if(test_res_t[i, j] < 0.05) {
grid.text("*", x, y)
} else if(test_res_t[i, j] < 0.2) {
grid.text("?", x, y)
}})
top 5
cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug %in% c(" pamidronate", "nimodipine", "moxifloxacin", "saxagliptin", "icosapent", "temozolomide"),]
p2 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, y= drug, color= drug))+ geom_point(stat = "identity") +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)
p1 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, fill= drug)) + geom_density(alpha=.2) +theme_minimal() + theme(
axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
plot_grid(p1, p2, ncol = 1, align = "v")
NA
Location of final scripts:
/script
Location of data produced:
na
Dates when operations were done:
220901
sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ComplexHeatmap_2.10.0 circlize_0.4.15 viridis_0.6.2
## [4] viridisLite_0.4.1 cowplot_1.1.1 ggplot2_3.3.6
## [7] tidyr_1.2.1 readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] png_0.1-7 assertthat_0.2.1 digest_0.6.29
## [4] foreach_1.5.2 utf8_1.2.2 R6_2.5.1
## [7] stats4_4.1.3 evaluate_0.16 highr_0.9
## [10] pillar_1.8.1 GlobalOptions_0.1.2 rlang_1.0.6
## [13] rstudioapi_0.13 jquerylib_0.1.4 S4Vectors_0.32.4
## [16] GetoptLong_1.0.5 rmarkdown_2.16 labeling_0.4.2
## [19] stringr_1.4.1 bit_4.0.4 munsell_0.5.0
## [22] compiler_4.1.3 xfun_0.33 BiocGenerics_0.40.0
## [25] pkgconfig_2.0.3 shape_1.4.6 htmltools_0.5.3
## [28] tidyselect_1.1.2 tibble_3.1.8 gridExtra_2.3
## [31] IRanges_2.28.0 codetools_0.2-18 matrixStats_0.62.0
## [34] fansi_1.0.3 crayon_1.5.2 dplyr_1.0.10
## [37] tzdb_0.3.0 withr_2.5.0 jsonlite_1.8.0
## [40] gtable_0.3.1 lifecycle_1.0.2 DBI_1.1.3
## [43] magrittr_2.0.3 scales_1.2.1 cli_3.4.1
## [46] stringi_1.7.8 vroom_1.5.7 cachem_1.0.6
## [49] farver_2.1.1 doParallel_1.0.17 bslib_0.4.0
## [52] ellipsis_0.3.2 generics_0.1.3 vctrs_0.4.2
## [55] RColorBrewer_1.1-3 rjson_0.2.21 iterators_1.0.14
## [58] tools_4.1.3 bit64_4.0.5 glue_1.6.2
## [61] purrr_0.3.4 hms_1.1.2 parallel_4.1.3
## [64] fastmap_1.1.0 yaml_2.3.5 clue_0.3-61
## [67] colorspace_2.0-3 cluster_2.1.2 knitr_1.40
## [70] sass_0.4.2